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Abstract

Lung neoplasms account for the highest cancer mortality globally. The present study aimed to conduct a preliminary assessment of several studies utilizing sequencing data to examine the genetic profiles of cancer patients and healthy individuals. The findings were analyzed through a meta-analysis approach to identify a shared common gene. The gene of interest was analyzed statistically using selected studies. The BCL2 gene family, with an FDR-P value below 0.01, was selected and identified as a common gene family using the CLC Genomics Workbench 9 software. The BCL2L1 gene was identified as the most significant gene among all the data based on statistical analysis conducted on the isoforms of the BCL2 gene family from each sample using the comprehnsive Meta-Analysis V3 software. The gene involved in the cancer pathway was identified using the KEGG website. In conclusion, the BCL2L1 gene is pivotal in cancer pathogenesis. Hence, by analyzing the gene pathways in which the BCL2L1 gene was implicated and its interactions with other proteins, it can therefore, be considered a potent gene in cancer research.

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2025-04-29
2025-09-08

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References

  1. Li X. Zhou X. Li Y. Activating transcription factor 3 promotes malignance of lung cancer cells in vitro. Thorac. Cancer 2017 8 3 181 191 10.1111/1759‑7714.12421 28239957
    [Google Scholar]
  2. Bade B.C. Dela Cruz C.S. Lung Cancer 2020. Clin. Chest Med 2020 41 1 1 24 10.1016/j.ccm.2019.10.001 32008623
    [Google Scholar]
  3. Sung H. Ferlay J. Siegel R.L. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021 71 3 209 249 10.3322/caac.21660 33538338
    [Google Scholar]
  4. Blandin Knight S. Crosbie P.A. Balata H. Chudziak J. Hussell T. Dive C. Progress and prospects of early detection in lung cancer. Open Biol. 2017 7 9 170070 10.1098/rsob.170070 28878044
    [Google Scholar]
  5. de Martel C. Georges D. Bray F. Ferlay J. Clifford G.M. Global burden of cancer attributable to infections in 2018: A worldwide incidence analysis. Lancet Glob. Health 2020 8 2 e180 e190 10.1016/S2214‑109X(19)30488‑7 31862245
    [Google Scholar]
  6. Molina JR Yang P Cassivi SD Non-small cell lung cancer: Epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc 2008 83 5 584 10.4065/83.5.584
    [Google Scholar]
  7. Marshall E.A. Ng K.W. Kung S.H.Y. Emerging roles of T helper 17 and regulatory T cells in lung cancer progression and metastasis. Mol. Cancer 2016 15 1 67 10.1186/s12943‑016‑0551‑1 27784305
    [Google Scholar]
  8. Nouri Z. Fakhri S. Nouri K. Wallace C.E. Farzaei M.H. Bishayee A. Targeting multiple signaling pathways in cancer: The rutin therapeutic approach. Cancers 2020 12 8 2276 10.3390/cancers12082276 32823876
    [Google Scholar]
  9. Alexander M. Kim S.Y. Cheng H. Update 2020: Management of non-small cell lung cancer. Lung 2020 198 6 897 907 10.1007/s00408‑020‑00407‑5 33175991
    [Google Scholar]
  10. Wu X. Li J. Du W. Causes of death following small cell lung cancer diagnosis: A population-based analysis. BMC Pulm. Med. 2022 22 1 262 10.1186/s12890‑022‑02053‑4 35787685
    [Google Scholar]
  11. Han Q. Ye M. Jin Q. Demethylzeylasteral inhibits proliferation, migration and invasion and promotes apoptosis of non-small cell lung cancer cells by inhibiting the AKT/CREB signalling pathway J Southern. Med. Univ. 2024 44 2 280 288
    [Google Scholar]
  12. Li X. Zhang D. Li B. Clinical implications of germline BCL2L11 deletion polymorphism in pretreated advanced NSCLC patients with osimertinib therapy. Lung Cancer 2021 151 39 43 10.1016/j.lungcan.2020.12.002 33296806
    [Google Scholar]
  13. Mir M.H. Siraj F. Mehfooz N. Clinicopathological profile of non-small cell lung cancer and the changing trends in its histopathology: Experience from a tertiary care cancer center in Kashmir, India. Cureus 2023 15 1 e34120 10.7759/cureus.34120 36843703
    [Google Scholar]
  14. Yang IA Holloway JW Fong KM Genetic susceptibility to lung cancer and co-morbidities. J Thorac Dis 2013 5 (Suppl 5)(Suppl.5) S454 62 24163739
    [Google Scholar]
  15. Kanwal M. Ding X.J. Cao Y. Familial risk for lung cancer. Oncol. Lett. 2017 13 2 535 542 10.3892/ol.2016.5518 28356926
    [Google Scholar]
  16. Anagnostou V.K. Lowery F.J. Zolota V. High expression of BCL-2 predicts favorable outcome in non-small cell lung cancer patients with non squamous histology. BMC Cancer 2010 10 1 186 10.1186/1471‑2407‑10‑186 20459695
    [Google Scholar]
  17. Yokota J. Shiraishi K. Kohno T. Genetic basis for susceptibility to lung cancer: Recent progress and future directions. Adv. Cancer Res. 2010 109 51 72 10.1016/B978‑0‑12‑380890‑5.00002‑8 21070914
    [Google Scholar]
  18. Fan J. Xia X. Fan Z. Hsa_circ_0129047 regulates the miR ‐375/ACVRL1 axis to attenuate the progression of lung adenocarcinoma. J. Clin. Lab. Anal. 2022 36 9 e24591 10.1002/jcla.24591 35908770
    [Google Scholar]
  19. Marcus M.W. Raji O. Duffy S.W. Young R.P. Hopkins R.J. Field J.K. Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model. Int. J. Oncol. 2016 49 1 361 370 10.3892/ijo.2016.3499 27121382
    [Google Scholar]
  20. Bai S. Cui S. Wen W. Tanshinone IIA suppresses non-small cell lung cancer through beclin-1-mediated autophagic apoptosis. Engineering (Beijing) 2022 19 128 138 10.1016/j.eng.2021.07.014
    [Google Scholar]
  21. Parodi M. Centonze G. Murianni F. Hybrid epithelial-mesenchymal status of lung cancer dictates metastatic success through differential interaction with NK cells. J. Immunother. Cancer 2024 12 3 e007895 10.1136/jitc‑2023‑007895 38458638
    [Google Scholar]
  22. Jin W. Lu S. Wang X. Shu Y. Shi H. Raddeanin A suppresses lung cancer cell proliferation via induction of apoptosis and increased production of ROS. Cell. Mol. Biol. 2020 66 7 174 179 10.14715/cmb/2020.66.7.26 33287938
    [Google Scholar]
  23. Lau A.N. Curtis S.J. Fillmore C.M. Tumor-propagating cells and Yap/Taz activity contribute to lung tumor progression and metastasis. EMBO J. 2014 33 5 468 481 10.1002/embj.201386082 24497554
    [Google Scholar]
  24. Park H.K. Han J. Kwon G.Y. Yeo M.K. Bae G.E. Patterns of extrathoracic metastasis in lung cancer patients. Curr. Oncol. 2022 29 11 8794 8801 10.3390/curroncol29110691 36421344
    [Google Scholar]
  25. Bi W. Cai S. Hang Z. Transplantation of feces from mice with Alzheimer’s disease promoted lung cancer growth. Biochem. Biophys. Res. Commun. 2022 600 67 74 10.1016/j.bbrc.2022.01.078 35196629
    [Google Scholar]
  26. Lv F Sun L Yang Q Prognostic value of BIM deletion in EGFR-Mutant NSCLC patients treated with EGFR-TKIs: A metaanalysis. Biomed Res Int 2021 2021 3621828 10.1155/2021/3621828
    [Google Scholar]
  27. Warren C.F.A. Wong-Brown M.W. Bowden N.A. BCL-2 family isoforms in apoptosis and cancer. Cell Death Dis. 2019 10 3 177 10.1038/s41419‑019‑1407‑6 30792387
    [Google Scholar]
  28. Senichkin V.V. Pervushin N.V. Zuev A.P. Zhivotovsky B. Kopeina G.S. Targeting Bcl-2 family proteins: What, where, when? Biochemistry (Mosc.) 2020 85 10 1210 1226 10.1134/S0006297920100090 33202206
    [Google Scholar]
  29. Kaloni D. Diepstraten S.T. Strasser A. Kelly G.L. BCL-2 protein family: Attractive targets for cancer therapy. Apoptosis 2023 28 1-2 20 38 10.1007/s10495‑022‑01780‑7 36342579
    [Google Scholar]
  30. Certo M. Moore V.D.G. Nishino M. Mitochondria primed by death signals determine cellular addiction to antiapoptotic BCL-2 family members. Cancer Cell 2006 9 5 351 365 10.1016/j.ccr.2006.03.027 16697956
    [Google Scholar]
  31. Pena J.C. Thompson C.B. Recant W. Vokes E.E. Rudin C.M. Bcl-xL and Bcl-2 expression in squamous cell carcinoma of the head and neck. Cancer 1999 85 1 164 170 10.1002/(SICI)1097‑0142(19990101)85:1<164:AID‑CNCR23>3.0.CO;2‑Q 9921989
    [Google Scholar]
  32. Gasteiger E. Wilkins M.R. Bairoch A. Protein identification and analysis tools on the ExPASy server. Methods Mol. Biol. 2005 112 531 10.1385/1‑59259‑890‑0:571
    [Google Scholar]
  33. Letai A.G. Diagnosing and exploiting cancer’s addiction to blocks in apoptosis. Nat. Rev. Cancer 2008 8 2 121 132 10.1038/nrc2297 18202696
    [Google Scholar]
  34. Akl H. Vervloessem T. Kiviluoto S. A dual role for the anti-apoptotic Bcl-2 protein in cancer: Mitochondria versus endoplasmic reticulum. Biochim. Biophys. Acta 2014 1843 10 2240 10.1016/j.bbamcr.2014.04.017
    [Google Scholar]
  35. Stamati L. Avgeris M. Kosmidis H. Overexpression of BCL2 and BAX following BFM induction therapy predicts ch-ALL patients’ poor response to treatment and short-term relapse. J. Cancer Res. Clin. Oncol. 2015 141 11 2023 2036 10.1007/s00432‑015‑1982‑6 25982455
    [Google Scholar]
  36. Han R. Hao S. Lu C. Aspirin sensitizes osimertinib‐resistant NSCLC cells in vitro and in vivo via Bim‐dependent apoptosis induction. Mol. Oncol. 2020 14 6 1152 1169 10.1002/1878‑0261.12682 32239624
    [Google Scholar]
  37. Deng J. Carlson N. Takeyama K. Dal Cin P. Shipp M. Letai A. BH3 profiling identifies three distinct classes of apoptotic blocks to predict response to ABT-737 and conventional chemotherapeutic agents. Cancer Cell 2007 12 2 171 185 10.1016/j.ccr.2007.07.001 17692808
    [Google Scholar]
  38. Qian S. Wei Z. Yang W. Huang J. Yang Y. Wang J. The role of BCL-2 family proteins in regulating apoptosis and cancer therapy. Front. Oncol. 2022 12 985363 10.3389/fonc.2022.985363 36313628
    [Google Scholar]
  39. Ma G. Deng Y. Qian L. Overcoming acquired resistance to third-generation EGFR inhibitors by targeting activation of intrinsic apoptotic pathway through Mcl-1 inhibition, Bax activation, or both. Oncogene 2022 41 12 1691 1700 10.1038/s41388‑022‑02200‑5 35102249
    [Google Scholar]
  40. Thomas S. Quinn B.A. Das S.K. Targeting the Bcl-2 family for cancer therapy. Expert Opin. Ther. Targets 2013 17 1 61 75 10.1517/14728222.2013.733001 23173842
    [Google Scholar]
  41. Saleh T. Carpenter V.J. Tyutyunyk-Massey L. Clearance of therapy‐induced senescent tumor cells by the senolytic ABT‐263 via interference with BCL-XL-BAX interaction. Mol. Oncol. 2020 14 10 2504 2519 10.1002/1878‑0261.12761 32652830
    [Google Scholar]
  42. Gala J.L. Vermylen C. Cornu G. High expression of bcl-2 is the rule in acute lymphoblastic leukemia, except in Burkitt subtype at presentation, and is not correlated with the prognosis. Ann. Hematol. 1994 69 1 17 24 10.1007/BF01757343 8061103
    [Google Scholar]
  43. Hermine O. Haioun C. Lepage E. Prognostic significance of bcl-2 protein expression in aggressive non-Hodgkin’s lymphoma. Blood 1996 87 1 265 8547651
    [Google Scholar]
  44. Henriksen R. Wilander E. Oberg K. Expression and prognostic significance of Bcl-2 in ovarian tumours. Br. J. Cancer 1995 72 5 1324 1329 10.1038/bjc.1995.509 7577491
    [Google Scholar]
  45. Karnak D. Xu L. Chemosensitization of prostate cancer by modulating Bcl-2 family proteins. Curr. Drug Targets 2010 11 6 699 707 10.2174/138945010791170888 20298153
    [Google Scholar]
  46. Perdomo C Campbell J Gerrein J Abstract 3173: Identification of miR-4423 as a primate-specific microRNA highly expressed in airway epithelium and associated with lung cancer. Cancer Res 2012 72 (8_Supplement)(Suppl.) 3173 3 10.1158/1538‑7445.AM2012‑3173
    [Google Scholar]
  47. Hwangbo H. Choi E.O. Kim M.Y. Suppression of tumor growth and metastasis by ethanol extract of Angelica dahurica Radix in murine melanoma B16F10 cells. Biosci. Trends 2020 14 1 23 34 10.5582/bst.2019.01230 32092745
    [Google Scholar]
  48. Cao W. Ma L. Effect and mechanism of rapamycin on proliferation and apoptosis of human lung cancer cells. Cell. Mol. Biol. 2020 66 6 65 70 10.14715/cmb/2020.66.6.12 33040787
    [Google Scholar]
  49. Byun E.B. Song H.Y. Kim W.S. Protective effect of polysaccharides extracted from cudrania tricuspidata fruit against cisplatin-induced cytotoxicity in macrophages and a mouse model. Int. J. Mol. Sci. 2021 22 14 7512 10.3390/ijms22147512 34299130
    [Google Scholar]
  50. Sun H.H. Li Y.L. Jiang H. Yin X.H. Jin X.L. PRDX1 influences the occurrence and progression of liver cancer by inhibiting mitochondrial apoptosis pathway. Cell J. 2022 24 11 657 664 36377215
    [Google Scholar]
  51. Belluomini L. Caliò A. Giovannetti R. Molecular predictors of EGFR-mutant NSCLC transformation into LCNEC after frontline osimertinib: Digging under the surface. ESMO Open 2021 6 1 100028 10.1016/j.esmoop.2020.100028 33465742
    [Google Scholar]
  52. Deng S. Qian L. Liu L. Circular RNA ARHGAP5 inhibits cisplatin resistance in cervical squamous cell carcinoma by interacting with AUF1. Cancer Sci. 2023 114 4 1582 1595 10.1111/cas.15723 36632741
    [Google Scholar]
  53. Kanehisa M. Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 28 1 27 30 10.1093/nar/28.1.27 10592173
    [Google Scholar]
  54. Sayin V.I. Ibrahim M.X. Larsson E. Antioxidants accelerate lung cancer progression in mice. Sci. Transl. Med. 2014 6 221 10.1126/scitranslmed.3007653
    [Google Scholar]
  55. Zhang H. Qi J. Reyes J.M. Oncogenic deregulation of EZH2 as an opportunity for targeted therapy in lung cancer. Cancer Discov. 2016 6 9 1006 1021 10.1158/2159‑8290.CD‑16‑0164 27312177
    [Google Scholar]
  56. Kim S.C. Jung Y. Park J. A high-dimensional, deep-sequencing study of lung adenocarcinoma in female never-smokers. PLoS One 2013 8 2 e55596 10.1371/journal.pone.0055596 23405175
    [Google Scholar]
  57. Raskatov J.A. Nickols N.G. Hargrove A.E. Marinov G.K. Wold B. Dervan P.B. Gene expression changes in a tumor xenograft by a pyrrole-imidazole polyamide. Proc. Natl. Acad. Sci. USA 2012 109 40 16041 16045 10.1073/pnas.1214267109 22988074
    [Google Scholar]
  58. Szklarczyk D. Gable A.L. Lyon D. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019 47 D1 D607 D613 10.1093/nar/gky1131 30476243
    [Google Scholar]
  59. Geourjon C. Deléage G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 1995 11 6 681 684 10.1093/bioinformatics/11.6.681 8808585
    [Google Scholar]
  60. Combet C. Blanchet C. Geourjon C. Deléage G. NPS@: Network protein sequence analysis. Trends Biochem. Sci. 2000 25 3 147 150 10.1016/S0968‑0004(99)01540‑6 10694887
    [Google Scholar]
  61. Cui Z. Bao X. Liu Q. MicroRNA‐378‐3p/5p represses proliferation and induces apoptosis of oral squamous carcinoma cells via targeting KLK4. Clin. Exp. Pharmacol. Physiol. 2020 47 4 713 724 10.1111/1440‑1681.13235 31868942
    [Google Scholar]
  62. Dennis G. Jr Sherman B.T. Hosack D.A. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 2003 4 5 P3 10.1186/gb‑2003‑4‑5‑p3 12734009
    [Google Scholar]
  63. Kanehisa M. Furumichi M. Sato Y. Ishiguro-Watanabe M. Tanabe M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 2021 49 D1 D545 D551 10.1093/nar/gkaa970 33125081
    [Google Scholar]
  64. Dadsena S. Cuevas Arenas R. Vieira G. Brodesser S. Melo M.N. García-Sáez A.J. Lipid unsaturation promotes BAX and BAK pore activity during apoptosis. Nat. Commun. 2024 15 1 4700 10.1038/s41467‑024‑49067‑6 38830851
    [Google Scholar]
  65. Szklarczyk D. Morris J.H. Cook H. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2016 D1 D362 D368 27924014
    [Google Scholar]
  66. Szklarczyk D. Gable A.L. Nastou K.C. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021 49 D1 D605 D612 10.1093/nar/gkaa1074 33237311
    [Google Scholar]
  67. Szklarczyk D. Franceschini A. Wyder S. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015 43 D1 D447 D452 10.1093/nar/gku1003 25352553
    [Google Scholar]
  68. Bank P.D. Crystallography: Protein data bank. Nat. New Biol. 1971 233 10 1038 10.1038/newbio233223b0
    [Google Scholar]
  69. Dutta S. Zardecki C. Goodsell D.S. Berman H.M. Promoting a structural view of biology for varied audiences: An overview of RCSB PDB resources and experiences. J. Appl. Cryst. 2010 43 5 1224 1229 10.1107/S002188981002371X 20877496
    [Google Scholar]
  70. Artimo P Jonnalagedda M Arnold K ExPASy: SIB bioinformatics resource portal Nucleic Acids Res 2012 40 Web Server issue W597 603 22661580
    [Google Scholar]
  71. Bateman A. Martin M-J. Orchard S. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res. 2023 51 D1 D523 D531 10.1093/nar/gkac1052 36408920
    [Google Scholar]
  72. Emanuelsson O. Nielsen H. Brunak S. von Heijne G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 2000 300 4 1005 1016 10.1006/jmbi.2000.3903 10891285
    [Google Scholar]
  73. Bateman A. Martin M-J. Orchard S. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 2021 49 D1 D480 D489 10.1093/nar/gkaa1100 33237286
    [Google Scholar]
  74. Ikai A. Thermostability and aliphatic index of globular proteins. J. Biochem. 1980 88 6 1895 1898 7462208
    [Google Scholar]
  75. Wang X. The expanding role of mitochondria in apoptosis. Genes Dev. 2001 15 22 2922 2933 11711427
    [Google Scholar]
  76. Uhlén M. Fagerberg L. Hallström B.M. Tissue-based map of the human proteome. Science 2015 347 6220 1260419 10.1126/science.1260419 25613900
    [Google Scholar]
  77. Thul P.J. Åkesson L. Wiking M. A subcellular map of the human proteome. Science 2017 356 6340 eaal3321 10.1126/science.aal3321 28495876
    [Google Scholar]
  78. Zhou G. Xia J. OmicsNet: A web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res. 2018 46 W1 W514-22 10.1093/nar/gky510 29878180
    [Google Scholar]
  79. Picarda E. Ohaegbulam K.C. Zang X. Molecular pathways: Targeting B7-H3 (CD276) for human cancer immunotherapy. Clin. Cancer Res. 2016 22 14 3425 3431 10.1158/1078‑0432.CCR‑15‑2428 27208063
    [Google Scholar]
  80. Zhang J. Yang P.L. Gray N.S. Targeting cancer with small molecule kinase inhibitors. Nat. Rev. Cancer 2009 9 1 28 39 10.1038/nrc2559 19104514
    [Google Scholar]
  81. Li K. Wang Z. Non-coding RNAs: Key players in T cell exhaustion. Front. Immunol. 2022 13 959729 10.3389/fimmu.2022.959729 36268018
    [Google Scholar]
  82. Kale J. Osterlund E.J. Andrews D.W. BCL-2 family proteins: Changing partners in the dance towards death. Cell Death Differ. 2018 25 1 65 80 10.1038/cdd.2017.186 29149100
    [Google Scholar]
  83. Westaby D Jimenez-Vacas JM Figueiredo I Abstract B020: BCL2 expression is enriched in AR-independent advanced prostate cancer Cancer Res 2023 83 11_Supplement Suppl. B020 0 10.1158/1538‑7445.PRCA2023‑B020
    [Google Scholar]
  84. Tagawa S.T. Armstrong A.J. Krause B.J. Tolerability of [177Lu]Lu-PSMA-617 by treatment exposure in patients with metastatic castration-resistant prostate cancer (mCRPC): A VISION study subgroup analysis. JCO 2022 40 5047 7
    [Google Scholar]
  85. Thiagarajan P.S. Wu X. Zhang W. Transcriptomicmetabolomic reprogramming in EGFR-mutant NSCLC early adaptive drug escape linking TGF β 2-bioenergetics-mitochondrial priming. Oncotarget 2016 7 50 82013 82027 10.18632/oncotarget.13307 27852038
    [Google Scholar]
  86. He Y. Zhang X. Zhu M. Soluble PD-L1: A potential dynamic predictive biomarker for immunotherapy in patients with proficient mismatch repair colorectal cancer. J. Transl. Med. 2023 21 1 25 10.1186/s12967‑023‑03879‑0 36639643
    [Google Scholar]
  87. Wu X. Up-regulation of YPEL1 and YPEL5 and down-regulation of ITGA2 in erlotinib-treated EGFR-mutant non-small cell lung cancer: A bioinformatic analysis. Gene 2018 643 74 82 10.1016/j.gene.2017.12.003 29221754
    [Google Scholar]
  88. Brunetti O. Badalamenti G. De Summa S. Molecular characterization of a long-term survivor double metastatic non-small cell lung Cancer and pancreatic ductal adenocarcinoma treated with gefitinib in combination with gemcitabine plus nab-paclitaxel and mFOLFOX6 as first and second-line therapy. Cancers 2019 11 6 749 10.3390/cancers11060749 31146476
    [Google Scholar]
  89. Derks M. Bryan M.C. Rockx C. Discovery of a novel, first-in-class Bfl-1 BH3 mimetic with pro-apoptotic activity. Blood 2023 142 Suppl. 1 2824 4 10.1182/blood‑2023‑188516
    [Google Scholar]
  90. Wang G. Diepstraten S.T. Herold M.J. Last but not least: BFL-1 as an emerging target for anti-cancer therapies. Biochem. Soc. Trans. 2022 50 4 1119 1128 10.1042/BST20220153 35900226
    [Google Scholar]
  91. Westaby D. Jiménez-Vacas J.M. Figueiredo I. BCL2 expression is enriched in advanced prostate cancer with features of lineage plasticity. J. Clin. Invest. 2024 134 18 e179998 10.1172/JCI179998 39286979
    [Google Scholar]
  92. Corella AN Lucas JM Kaipainen A Abstract 2962: Targeting BCL2 as a therapeutic strategy in neuroendocrine prostate cancer. Cancer Res 2019 79 13_Supplement Suppl. 2962 2 10.1158/1538‑7445.AM2019‑2962
    [Google Scholar]
  93. Fultang N Bhagwat N Heiser D Abstract 420: Combination of the MCL1 inhibitor PRT1419 and SMARCA2 degrader PRT3789 shows combinatorial benefit in SMARCA4 deleted lung cancer Cancer Res 2022 82 12_Supplement Suppl 420 0 10.1158/1538‑7445.AM2022‑420
    [Google Scholar]
  94. Xia X Ye Z Ahmad S Yu Y Wei Y Editorial: Drug-induced immunogenic cell death patterns and anti-cancer treatment. Front Pharmacol 2023 14 1252168 10.3389/fphar.2023.1252168.
    [Google Scholar]
  95. Gao F Yin J Chen Y Guo C Hu H Su J Recent advances in aptamer-based targeted drug delivery systems for cancer therapy. Front Bioeng Biotechnol 2022 10 972933 10.3389/fbioe.2022.972933 36051580
    [Google Scholar]
  96. Tan J Xue Q Hu X Yang J Inhibitor of PD-1/PD-L1: A new approach may be beneficial for the treatment of idiopathic pulmonary fibrosis. J Transl Med 2024 22 1 95 10.1186/s12967‑024‑04884‑7 38263193
    [Google Scholar]
  97. Carreras J Kikuti YY Hiraiwa S High PTX3 expression is associated with a poor prognosis in diffuse large B‐cell lymphoma Cancer Sci 2022 113 1 334 48 10.1111/cas.15179 34706126
    [Google Scholar]
  98. Sun L Yang Y Li Y Zhang B Shi R The past, present, and future of liver cancer research in China Cancer Lett 2023 574 216334 10.1016/j.canlet.2023.216334 37574184
    [Google Scholar]
  99. Yap TA Fontana E Lee EK Camonsertib in DNA damage response-deficient advanced solid tumors Phase 1 trial results. Nat Med 2023 29 6 1400 11 10.1038/s41591‑023‑02399‑0 37277454
    [Google Scholar]
  100. He X Du T Long T Liao X Dong Y Huang ZP Signaling cascades in the failing heart and emerging therapeutic strategies Signal Transduct Target Ther 2022 7 1 134 10.1038/s41392‑022‑00972‑6 35461308
    [Google Scholar]
  101. Dan A Burtavel LM Coman MC Genetic blueprints in lung cancer: Foundations for targeted therapies Cancers 2024 16 23 4048. 10.3390/cancers16234048 39682234
    [Google Scholar]
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A Multi-study Meta-analysis Approach to Identifying Genetic Signatures in Lung Cancer Progression
Bentham Science Publishers ; https://doi.org/10.2174/0115733947367751250410105409
/content/journals/cctr/10.2174/0115733947367751250410105409
/content/journals/cctr/10.2174/0115733947367751250410105409
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  • Article Type:     Review Article
Keywords: sequencing data ; Lung neoplasms ; meta-analysis ; protien interaction networks ; BCL2L1 ; genetic profiles ; BCL2 gene family

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